Support Tensorrt-LLM MoE fp4 for low-latency

[wenscarl]

co-authored by: @xinli-git
patched partially #22073 to fix DSR1 weight loading.

export VLLM_USE_FLASHINFER_MOE_FP4=1
export VLLM_FLASHINFER_MOE_BACKEND="latency"
lm_eval --model vllm --model_args pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP4,tensor_parallel_size=4,max_model_len=2048,enforce_eager=True,kv_cache_dtype=auto --gen_kwargs temperature=0.0 --limit 500 --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size 200

Llama4:

vllm (pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP4,quantization=modelopt_fp4,tensor_parallel_size=1,max_model_len=2048,enforce_eager=True,kv_cache_dtype=auto,trust_remote_code=True), gen_kwargs: (temperature=0.0), limit: 500.0, num_fewshot: 5, batch_size: 200
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.896|±  |0.0137|
|     |       |strict-match    |     5|exact_match|↑  |0.888|±  |0.0141|

DSR1-FP4:

INFO:lm_eval.loggers.evaluation_tracker:Output path not provided, skipping saving results aggregated
vllm (pretrained=nvidia/DeepSeek-R1-FP4,quantization=modelopt_fp4,tensor_parallel_size=4,enforce_eager=True,max_model_len=2048,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9431|±  |0.0064|
|     |       |strict-match    |     5|exact_match|↑  |0.9424|±  |0.0064

Perf:

With TP=8, B200x8,
1k/8k
max-num-seq=4, num-req=8:
trtllm-gen:
Throughput: 0.12 requests/s, 362.62 total tokens/s, 241.74 output tokens/s
flashinfer cutlass:
Throughput: 0.09 requests/s, 285.77 total tokens/s, 190.51 output tokens/s
max-num-seq=8, num-req=16,
trtllm-gen:
Throughput: 0.04 requests/s, 371.03 total tokens/s, 329.80 output tokens/s
flashinfer cutlass:
Throughput: 0.04 requests/s, 358.38 total tokens/s, 318.56 output tokens/s
max-num-seq=16, num-req=32,
trtllm-gen:
Throughput: 0.09 requests/s, 845.02 total tokens/s, 751.13 output tokens/s
flashinfer cultass:
Throughput: 0.08 requests/s, 709.25 total tokens/s, 630.45 output tokens/s

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @wenscarl.

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[gemini-code-assist]

Code Review

This pull request adds support for TensorRT-LLM and FlashInfer CUTLASS FP4 MoE kernels, which is a great step for improving performance. The changes include a significant and well-structured refactoring of the MoE framework to be more modular.

However, I've found a few critical issues, primarily in the new TensorRT-LLM integration path. The implementation in flashinfer_trtllm_moe.py seems incomplete and will cause runtime errors due to undefined variables and unreachable code. Similarly, the invocation of this kernel in modelopt.py is missing arguments. These issues must be addressed before this PR can be merged.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @wenscarl.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @wenscarl.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[andoorve]

Took a preliminary look. I have limited context on this but in general looks good. The logic is fairly complex in some files (modelopt.py) and the MoE code is getting unwieldly overall. It would be good to look for some refactoring and simplification opportunities where possible. It would also be good to have some tests for coverage as well. There are also some places with missing return value typing.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @wenscarl.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[nvpohanh]

rebased again...